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• The algorithm obtains a spatially DNI estimation with a multi-robot sensor network. • The algorithm approaches both the generation of tasks and the estimation of DNI. • The clouds and exploring unknown areas are needed to generate new tasks. • The framework is modular and additional information sources can be considered. An algorithm for mapping Direct Normal Irradiance (DNI) in thermosolar power plants using a Mobile Robotic Sensor Network (RSN) is presented. The algorithm selects measurement spots and allocates the RSN accordingly to carry out the dynamic estimation of DNI. A generic thermosolar power plant with a fleet of vehicles is used as a simulated case study to assess the performance of the algorithm. The results show that the proposed method allows us to obtain a spatial estimation of the DNI that could improve the performance of the plant, and that outperforms estimations based on a single pyrheliometer. [ABSTRACT FROM AUTHOR]

This article considers a robotic sensor network that measures the loss of irradiance in a thermosolar power plant due to moving clouds. To this end, a receding-horizon predictive algorithm is proposed for multi-robot task allocation. Despite the high nonlinearity of the problem, the experiments carried out varying the horizon size show that the proposed method has a good performance with small horizons and tasks moving in similar directions, outperforming a previously published approach based on genetic algorithms. Finally, realistic simulations performed on a solar plant implemented in Robot Operating System/Gazebo prove the feasibility of the proposed method and its potential to provide significant performance gains with a much lower investment than an equivalent fixed sensor network. • Predictive receding-horizon multi-robot task allocation for irradiance mapping. • Allocation algorithm considering dynamic evolution of direct normal irradiance maps. • Computationally inexpensive algorithm based on linear programming relaxation. • Extensive simulation studies outperforming greedy/genetic algorithms. [ABSTRACT FROM AUTHOR]

Caffeic acid and its naturally occurring derivative caffeic acid phenethyl ester (CAPE) have antiproliferative and cytotoxic properties in a variety of cancer cell lines without displaying significant toxicity toward healthy cells, and are considered to be potential anticancer agents. However, little is known about their effects on prostate cancer cells. We synthesized and evaluated the effects of caffeic acid, CAPE (2) and 18 synthetic derivatives on cell viability and androgen-dependent cell proliferation, subcellular localisation and expression of androgen receptor (AR) and secretion of prostate-specific antigen (PSA) in LNCaP human hormone-dependent prostate cancer cells. Several synthetic derivatives of CAPE were strong, concentration-dependent cytotoxic agents in LNCaP cells with IC50 values in the 6.8–26.6μM range, potencies that were up to five-fold greater than that of CAPE (33.7±4.0μM). A number of caffeic acid derivatives were inhibitors of androgen-stimulated LNCaP cell proliferation with concomitant inhibition of DHT-stimulated PSA secretion. Compound 24 was the most cytotoxic and antiproliferative caffeic acid derivative (IC50 values of 6.8±0.3 and 2.4±0.8μM, respectively) inhibiting DHT-stimulated cell proliferation and PSA secretion statistically significantly at concentrations as low as 0.3μM. Exposure to DHT increased cytoplasmic and nuclear AR levels and co-treatment with increasing concentrations of compound 24 or CAPE (2), notably, further increased these levels. In conclusion, a number of synthetic derivatives of caffeic acid are potent inhibitors of androgen-dependent prostate cancer cell proliferation and viability, acting, at least in part, via an antiandrogenic mechanism that involves increased nuclear accumulation of (presumably inactive) AR. [ABSTRACT FROM AUTHOR]